Showing papers on "Partial autocorrelation function published in 1982"
TL;DR: This paper demonstrates the use of the generalized partial autocorrelation function (GPAF) and the R and S functions of Gray et al. (1978) for ARMA model identification of hydrologic time series.
Abstract: In recent years, ARMA models have become popular for modeling geophysical time series in general and hydrologic time series in particular. The identification of the appropriate order of the model is an important stage in ARMA modeling. Such model identification is generally based on the autocorrelation and partial autocorrelation functions, although recently improvements have been obtained using the inverse autocorrelation and the inverse partial autocorrelation functions. This paper demonstrates the use of the generalized partial autocorrelation function (GPAF) and the R and S functions of Gray et al. (1978) for ARMA model identification of hydrologic time series. These functions are defined, and some recursive relations are given for ease of computation. All three functions, when presented in tabular form, have certain characteristic patterns that are useful in ARMA model identification. Several examples are included to demonstrate the usefulness of the proposed identification technique. Actual applications are made using the Saint Lawrence River and Nile River annual streamflow series.
77 citations
1 Jan 1982
TL;DR: This paper will cover two speech synthesizer LSIs developed in C2MOS technology that uses the partial autocorrelation (Parcor) algorithm and the other is based on adaptive delta modulation.
Abstract: This paper will cover two speech synthesizer LSIs developed in C2MOS technology. One uses the partial autocorrelation (Parcor) algorithm and the other is based on adaptive delta modulation. The power dissipation is only 0.6 and 0.18mW, respectively.
8 citations
1 May 1982
TL;DR: In this article, the authors compared the Cramer-Rao bound generalized variances in estimating the poles and zeros of the ARMA system generating the process and showed that the choice of lags of the sample autocorrelation function required to preserve most of the information in the data is signal dependent.
Abstract: Many modern ARMA spectral estimators are based either on the raw data or on some version of the lagged-product sample autocorrelation function (ACF). These two classes are compared in terms of their Cramer-Rao bound generalized variances in estimating the poles and zeros of the ARMA system generating the process. It is seen that the choice of lags of the sample ACF required to preserve most of the information in the data is signal dependent. Recommendations of a "good" information-preserving choice of lags for an AR(2) process in white noise are tabulated against pole magnitude and SNR. The case of two additive narrowband AR(2) processes is also studied.
3 citations
1 May 1982
TL;DR: Two methods for AR spectrum analysis based on a noisy autocovariance sequence are presented, based on the least squares fit of the autocvariances to the Yule-Walker equations and the maximum likelihood estimation of partial autocorrelation coefficients by nonlinear optimization techniques.
Abstract: This paper presents two methods for AR spectrum analysis based on a noisy autocovariance sequence. Unlike most of the traditional spectrum analysis methods, we assume that there is an observed autocovariance sequence, possibly with errors. Such a situation occurs in the Fourier spectroscopy. We fit AR models to this autocovariance sequence by two methods. The one is based on the least squares fit of the autocovariances to the Yule-Walker equations and the other is based on the maximum likelihood estimation of partial autocorrelation coefficients by nonlinear optimization techniques. We apply these two methods to simulated and real plasma data and compare their results.
1 citations